Improving the Click Prediction for Online Advertisement with the Integration of Recommended System using Neural Network Architecture

Jayachandran, Jayalakshmi (2022) Improving the Click Prediction for Online Advertisement with the Integration of Recommended System using Neural Network Architecture. Masters thesis, Dublin, National College of Ireland.

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Abstract

Nowadays shopping for products through online platforms is highly inclining due to better internet and technologies across the globe. Online advertisements play an important role in such business strategies. It provides the access to customers to view a large variety of the products of their requirements. Customers make purchases based on the rating of products. Every day, millions of customers are posting their reviews online about the products. In this business strategy clicking on online advertisements is an important factor because the more clicks lead the more probability of making a purchase. A noble idea of online advertising based on the recommendation system can be more effective in such scenarios but still, it is a tedious task. Prediction of clicks on the online advertisements shown based on the recommendation system will help in understanding the requirements of customers and companies both. In order to solve such a task, a product-based recommendation system is implemented to identify the top products based on ratings, and these products are randomly combined with the click dataset in order to predict clicks on the shown advertisements. After data combination, data pre-processing, and feature extraction, three deep learning-based models are implemented. These models are trained over training data and then tested on test data for the evaluation based on the metrics such as Accuracy, PRF score, and MCC score. This evaluation results in the LSTM model being the best optimal model that can address such tedious tasks and can help in new online business strategies.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Marketing > Consumer Behaviour H Social Sciences > HF Commerce > Marketing > e Marketing Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Online Shopping T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Online Shopping
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	26 Jan 2023 17:45
Last Modified:	26 Jan 2023 17:45
URI:	https://norma.ncirl.ie/id/eprint/6144

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